University of Colorado Boulder

Association Rules Analysis

Di Wu

Instructor: Di Wu

2,359 already enrolled

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

22 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

22 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection

  • Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.

  • Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.

Details to know

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Assessments

5 assignments

Taught in English

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This course is part of the Data Analysis with Python Specialization
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There are 5 modules in this course

This week provides an introduction to unsupervised learning and association rules analysis. You will explore frequent itemsets, understanding their significance in discovering patterns in transactional data. You will also explore association rules, such as support, confidence, and lift metrics as key indicators of association rule quality.

What's included

2 videos4 readings1 assignment

This week we will briefly discuss association rule mining, such as closed and maxed patterns.

What's included

1 video1 assignment

This week focuses on the Apriori and FP Growth algorithm, a key method for efficient frequent itemset mining.

What's included

2 videos4 readings1 assignment1 discussion prompt

Throughout this week, you will explore the significance of outlier detection and its role in identifying unusual data points.

What's included

1 video2 readings1 assignment1 discussion prompt

The final week focuses on a comprehensive case study where you will apply association rule mining and outlier detection techniques to solve a real-world problem.

What's included

1 reading1 assignment1 discussion prompt

Instructor

Di Wu
University of Colorado Boulder
15 Courses40,679 learners

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